Online forums dedicated to collegiate athletic tournament forecasting, specifically on the Reddit platform, serve as hubs for individuals to share and discuss their projected outcomes. These communities often involve users submitting their forecasted tournament results, analyzing team statistics, and engaging in debates regarding potential upsets or pathways to the championship. A typical example includes a thread where users post screenshots of their completed bracket, followed by detailed explanations of their rationale behind specific team selections.
The significance of these online communities lies in their ability to aggregate collective knowledge and diverse perspectives, potentially leading to more informed forecasts. The open nature of the platform facilitates a dynamic exchange of ideas, allowing participants to refine their strategies based on feedback and evolving information. Historically, such forums have mirrored broader trends in sports analytics, transitioning from purely subjective assessments to incorporating data-driven models and advanced statistical analyses.
The following sections will delve into common prediction methodologies employed, the potential pitfalls inherent in relying on group consensus, and the ethical considerations surrounding information sharing within these competitive online spaces. Additionally, the analysis will examine the impact of these shared forecasts on broader betting markets and fan engagement with the annual tournament.
1. Community Size
The number of participants within online collegiate tournament forecasting forums significantly impacts the diversity of opinions and the volume of information exchanged, directly affecting the nature of the predictions generated.
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Diversity of Perspectives
Larger communities inherently incorporate a wider range of viewpoints and analytical approaches. This includes differing levels of statistical expertise, varying degrees of team familiarity, and a broader spectrum of biases, leading to more robust debates and potentially more refined forecasts. For example, a community with a substantial membership might include individuals with in-depth knowledge of specific conferences or teams that would otherwise be overlooked.
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Information Aggregation
A larger user base facilitates the rapid aggregation and dissemination of relevant information. This includes news regarding player injuries, coaching changes, and team performance trends. Members contribute links to statistical analyses, scouting reports, and game highlights, enriching the overall resource pool available to participants. A larger community can more effectively monitor and synthesize this disparate information.
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Amplification of Bias
While diversity can be beneficial, larger communities are also susceptible to the amplification of pre-existing biases. A commonly held belief, even if unsubstantiated, can gain traction and influence predictions, particularly among less experienced participants. This phenomenon, known as groupthink, can lead to a convergence on less accurate forecasts. The echo chamber effect, where opinions are reinforced by repetition, is more pronounced in larger, less moderated communities.
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Computational Power of Collective Intelligence
The aggregated analytical power of a large community, while not formally structured, can approximate a form of collective intelligence. Members can collectively identify and correct errors in statistical models, challenge flawed assumptions, and collaboratively refine prediction algorithms. This iterative process, driven by peer review and open debate, can lead to the development of more sophisticated and accurate forecasting methodologies.
The size of the online collegiate tournament forecasting community thus presents a trade-off between the benefits of diverse perspectives and information aggregation and the risks of amplified bias and groupthink. The effectiveness of a community in generating accurate predictions depends on its ability to harness the collective intelligence of its members while mitigating the negative consequences of unchecked biases and misinformation.
2. Prediction Accuracy
The accuracy of forecasts shared within online collegiate tournament communities directly reflects the efficacy of the methodologies employed and the quality of information disseminated. Evaluating forecast accuracy is crucial for determining the value of participating in, or relying on, these online forums.
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Methodological Rigor and Statistical Modeling
The predictive accuracy of forecasts generated within online communities often depends on the rigor of the underlying methodologies and the sophistication of statistical models used. Communities that emphasize data-driven analysis, incorporating factors such as team performance metrics, player statistics, and strength of schedule, tend to yield more accurate predictions. The absence of robust statistical models or an over-reliance on subjective assessments can significantly reduce forecast accuracy. For example, communities that merely reflect popular sentiment without grounding predictions in empirical data are generally less reliable.
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Information Quality and Timeliness
The quality and timeliness of information available to participants directly influence forecast accuracy. Access to up-to-date data on player injuries, coaching changes, and other relevant factors enables more informed predictions. Conversely, reliance on outdated or inaccurate information can lead to flawed forecasts. An example includes a community that promptly incorporates news of a key player’s injury into its analyses, resulting in a more accurate assessment of the affected team’s chances.
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Community Dynamics and Peer Review
The dynamics within the online community, specifically the degree of peer review and constructive criticism, impact prediction accuracy. Communities that foster open debate and encourage members to challenge assumptions tend to produce more refined and accurate forecasts. The ability to identify and correct errors in statistical models or highlight flawed reasoning contributes to improved predictive performance. A forum where users actively scrutinize each other’s methodologies is more likely to generate reliable predictions.
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Accounting for Unpredictability and Variance
Even with rigorous methodologies and high-quality information, the inherent unpredictability of sporting events introduces variance into forecast accuracy. Online communities that acknowledge and account for this unpredictability, incorporating elements of chance or Monte Carlo simulations, may generate more realistic and nuanced predictions. The inability to fully capture the inherent randomness of sporting events remains a limitation, and communities should temper expectations regarding perfect forecast accuracy.
The overall accuracy of collegiate tournament predictions within online forums is a complex function of methodological rigor, information quality, community dynamics, and the inherent unpredictability of the sport. Evaluating these factors is essential for assessing the value and reliability of the forecasts generated within these communities.
3. Information Source
The veracity and origin of data underpinning collegiate tournament forecasts shared within online communities are paramount to the reliability of those predictions. The quality of the information source directly impacts the analytical rigor of the projections and, consequently, their potential for accuracy. These sources range from official team statistics and injury reports to less verifiable fan forums and anecdotal observations. The reliance on credible sources is a critical component in the forecasting process within platforms like Reddit.
For example, predictions based on official NCAA data, KenPom ratings, or ESPN analytics tend to possess a higher degree of trustworthiness compared to forecasts solely derived from subjective assessments found on unverified discussion boards. A practical application of this understanding lies in the ability to discern between well-supported projections grounded in empirical evidence and those based on anecdotal evidence or unsubstantiated claims. Communities that prioritize verifiable information demonstrate a commitment to rigorous analysis, fostering a more informed and reliable predictive environment. The access and proper interpretation of these sources is the cornerstone of credible analysis.
Ultimately, the value of collegiate tournament predictions within online communities is inextricably linked to the quality of the information sources utilized. Challenges arise in identifying and filtering credible data from the deluge of available information. The ongoing pursuit of reliable information and the development of critical evaluation skills are essential for individuals seeking to leverage these online forums effectively. Recognizing the importance of verified and high-quality sources for building better ncaa bracket predictions within these communities is also very important.
4. Popularity Bias
Popularity bias, the tendency to favor well-known or highly regarded teams regardless of their statistical probability of success, exerts a significant influence on forecasts shared within online collegiate tournament communities. The presence of this bias can skew aggregate predictions towards more recognizable brands, potentially diminishing the accuracy of overall projections. For example, a team with a strong historical reputation, even if currently underperforming relative to its seed, may be overvalued in bracket submissions due to its enduring popularity among casual fans. This results in a deviation from predictions grounded solely in objective metrics.
The effect of popularity bias is amplified on platforms like Reddit, where the visibility of individual predictions is often correlated with upvotes or positive feedback. Submissions that conform to popular sentiment, selecting favored teams to advance further in the tournament, may receive disproportionately more attention, regardless of their underlying analytical merit. This creates a feedback loop, reinforcing the bias and potentially discouraging users from submitting contrarian predictions based on more nuanced analysis. A practical demonstration of this phenomenon can be observed in instances where statistically superior but less publicized mid-major teams are consistently overlooked in favor of higher-seeded but more popular programs.
Mitigating the impact of popularity bias within collegiate tournament forecasting communities necessitates a greater emphasis on data-driven analysis and critical evaluation of information. Encouraging participants to challenge conventional wisdom and to ground their predictions in empirical evidence, rather than subjective assessments or emotional attachments, can lead to more accurate and informative forecasts. Acknowledging and addressing the inherent biases that influence individual and collective predictions is crucial for improving the overall quality and reliability of online tournament forecasting communities. The ability to recognize and account for it improves one’s understanding of the ncaa bracket predictions discussed.
5. Statistical Models
Statistical models form a cornerstone of predictive analytics within online collegiate tournament forecasting communities. These models, ranging from simple ranking systems to complex machine learning algorithms, provide a quantitative framework for assessing team strengths and simulating tournament outcomes. The application of statistical models within platforms like Reddit serves to temper subjective biases and introduce a degree of empirical rigor to the prediction process. For example, models incorporating team offensive and defensive efficiency ratings, strength of schedule adjustments, and historical performance data offer a more nuanced assessment than solely relying on team seeding or anecdotal observations. This allows individuals participating in these communities to generate informed predictions based on data-driven insights.
The effectiveness of statistical models in predicting tournament outcomes is influenced by several factors, including the quality and granularity of the input data, the sophistication of the model itself, and the degree to which the model accounts for unpredictable elements inherent in sporting events. Models that incorporate advanced features, such as player-specific statistics, injury reports, and opponent-adjusted performance metrics, tend to exhibit greater predictive accuracy. Furthermore, the regular updating and refinement of these models based on recent performance data are crucial for maintaining their effectiveness. A practical application is the use of Monte Carlo simulations, which run thousands of tournament scenarios based on model-derived probabilities, providing a more comprehensive view of potential outcomes than a single bracket projection.
In summary, statistical models play a crucial role in enhancing the analytical foundation of collegiate tournament forecasting within online communities. The adoption of these models promotes a more objective and data-informed approach to prediction, mitigating the influence of biases and improving the overall accuracy of forecasts. However, challenges remain in developing models that accurately capture the inherent unpredictability of tournament outcomes, and individuals should recognize that statistical models are tools to inform, not guarantee, accurate predictions. The integration and understanding of these statistical models are crucial for those participating in and analyzing the discussions on sites like Reddit concerning collegiate tournament forecasts, where the goal is to produce the most accurate ncaa bracket predictions.
6. Upsets Discussion
The analysis of potential upsets constitutes a significant element within collegiate tournament forecast discussions on platforms like Reddit. The very nature of the single-elimination tournament format elevates the impact of unexpected outcomes, rendering the accurate prediction of upsets critical for successful bracket construction. These discussions frequently center on identifying lower-seeded teams with characteristics indicative of upset potential, considering factors such as favorable matchups, key player injuries on higher-seeded opponents, or statistical anomalies suggesting undervalued performance. An example includes detailed analyses of specific team matchups, highlighting statistical advantages held by the lower-seeded team, leading to a consensus forecast of an upset victory. This process enhances user engagement and adds a layer of complexity to the overall forecasting endeavor.
The importance of upsets discussion stems from its direct influence on bracket scoring strategies. While correctly predicting high-seed victories contributes to overall accuracy, the identification of successful upsets provides a disproportionate advantage, particularly in bracket pools with point multipliers for later rounds. Discussions frequently involve risk-reward assessments, weighing the potential gains of accurately forecasting an upset against the risk of an early bracket bust. For instance, users may debate whether to select a 12-seed over a 5-seed, considering the 12-seed’s recent performance against similar opponents and the potential for a significant point boost should the upset occur. The volume and diversity of opinions relating to potential upsets on Reddit offers a unique aggregation of crowd-sourced insights, potentially improving the accuracy of bracket selections beyond simple reliance on seeding or historical data.
The inherent challenge lies in differentiating genuine upset opportunities from statistical noise or wishful thinking. The discussions often rely on a blend of quantitative analysis and qualitative judgment, with users presenting statistical data to support their arguments while also considering intangible factors such as team momentum and coaching experience. Despite the potential for increased accuracy, overemphasizing upset predictions can lead to bracket instability and diminished overall performance. Therefore, a balanced approach, integrating statistical analysis with informed consideration of potential upsets, is essential for maximizing bracket success within these online communities. The quality and frequency of upset discussions contributes significantly to the overall appeal and competitive nature of collegiate tournament forecasting on Reddit, influencing the final ncaa bracket predictions users choose to embrace.
7. Consensus Building
Consensus building is a core dynamic within online collegiate tournament forecast communities, influencing the collective predictive accuracy and shaping individual bracket construction strategies. The aggregation of diverse opinions and analyses contributes to a shared understanding of potential tournament outcomes, with varying degrees of impact on final predictions.
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Aggregation of Statistical Insights
Online forums serve as repositories for a wide range of statistical analyses, ranging from team efficiency ratings to player-specific metrics. Consensus emerges as users share, critique, and refine these analyses, leading to a more comprehensive understanding of team strengths and weaknesses. For example, a user might highlight a team’s exceptional three-point shooting percentage, prompting others to investigate further and incorporate this factor into their predictions. This collaborative approach can lead to a more accurate assessment of a team’s upset potential or likelihood of advancing deep into the tournament.
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Identification of Key Variables
Consensus building often involves the identification and weighting of key variables that influence tournament outcomes. Factors such as recent performance, injury reports, and strength of schedule are frequently debated and incorporated into collective forecasts. The relative importance assigned to each variable is often determined through iterative discussions, with users presenting evidence to support their claims. A consensus might emerge regarding the significance of a particular player’s injury, leading to a downward adjustment in the affected team’s projected performance. The integration of multiple factors contributes to a holistic and nuanced prediction model.
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Mitigation of Individual Biases
Online communities can help mitigate individual biases that might skew bracket predictions. By exposing users to diverse perspectives and challenging their assumptions, the consensus-building process encourages a more objective evaluation of team prospects. For instance, a user with a strong affinity for a particular team might be confronted with counterarguments based on statistical data or opposing viewpoints, leading to a reassessment of their initial prediction. The open exchange of ideas fosters a more balanced and rational forecasting approach.
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Amplification of Groupthink
Despite its potential benefits, consensus building can also lead to the amplification of groupthink, where dissenting opinions are suppressed or ignored in favor of conforming to the prevailing viewpoint. This phenomenon can result in the overvaluation of popular teams and the underestimation of potential upsets. For example, a widely held belief about a team’s invincibility might discourage users from considering alternative scenarios, even if supported by statistical evidence. A critical awareness of groupthink is essential for maintaining independent judgment and avoiding the pitfalls of collective bias.
These aspects collectively showcase that consensus building on collegiate tournament prediction platforms provides a complex interplay of shared information, individual biases, and statistical insights, ultimately shaping the bracket projections observed within the online community. Recognizing these dynamics is crucial for both generating and evaluating forecasts on platforms like Reddit.
8. Betting Influence
The correlation between online collegiate tournament forecasts and betting markets is substantial. Platforms where individuals share their predicted tournament outcomes can indirectly influence betting odds and patterns. Increased public awareness of, and confidence in, specific team outcomes, as disseminated through these communities, may lead to a corresponding increase in betting volume on those teams. This elevated betting activity can, in turn, alter the odds offered by bookmakers. For instance, if a substantial consensus emerges within a prominent online forum regarding a particular underdog’s likelihood of victory, a subsequent surge in bets on that underdog might reduce their payout odds.
The dissemination of expert analysis and statistical modeling within these communities further contributes to the betting influence. Individuals who utilize such forums to inform their wagering decisions may be more inclined to place larger or more strategic bets, based on the insights gained. The availability of aggregated data and collective analysis can empower bettors to make more informed decisions, potentially shifting the balance of power from bookmakers to informed participants. Consider the example of a regression analysis published on a popular platform that reveals a specific statistical advantage held by a lower-seeded team. This information could lead to a significant increase in bets placed on that team to advance, regardless of their initial odds.
In conclusion, the exchange of tournament forecasts within online communities has a demonstrable influence on betting markets. The collective wisdom, or perceived wisdom, shared on these platforms can shape public perception, alter betting volumes, and ultimately impact the odds offered by bookmakers. While the precise magnitude of this influence is difficult to quantify, the link between online forecast communities and betting behavior is undeniable. Recognizing this connection is crucial for both individuals participating in tournament pools and those engaged in more formal wagering activities, allowing them to understand the potential impact of collective forecasts on market dynamics. The analysis of ncaa bracket predictions on Reddit, therefore, has real-world financial implications for its participants and observers.
9. Algorithm Usage
Algorithm usage within online collegiate tournament forecast communities represents a fundamental shift from purely subjective prediction methods towards data-driven, quantitative analysis. These algorithms, often shared and debated on platforms like Reddit, range in complexity from simple Elo rating systems to sophisticated machine learning models that incorporate a multitude of variables. Their application aims to reduce the influence of bias and improve the accuracy of bracket predictions by leveraging historical data, team statistics, and other relevant factors. For example, a user might post an algorithm that weights a team’s offensive and defensive efficiency, strength of schedule, and recent performance, resulting in a predictive power ranking used to simulate potential tournament outcomes. The reliance on algorithmic analysis introduces a level of rigor and repeatability that is absent in purely qualitative assessments.
The impact of algorithm usage is evident in the competitive landscape of bracket pools. Participants who employ data-driven models often outperform those who rely solely on intuition or popular opinion, particularly in large-scale tournaments with significant prize pools. These algorithms can identify undervalued teams or potential upset candidates that are overlooked by conventional wisdom, providing a competitive edge to those who leverage them effectively. For example, analysis using algorithmic methods may reveal that a 12-seed exhibits statistical characteristics similar to teams that have historically upset 5-seeds. Sharing such algorithmically derived insights within a community allows other users to refine their strategies and improve their bracket selections, increasing overall community predictive accuracy.
However, challenges exist in the widespread adoption and effective application of algorithmic analysis. The complexity of some models can be a barrier to entry for less technically inclined users, and the reliance on historical data can be limiting in situations where unexpected events or significant team changes occur. Furthermore, the pursuit of optimal algorithms can lead to overfitting, where models are tailored too closely to past data and fail to generalize to future tournament outcomes. Despite these challenges, algorithm usage remains a crucial component of competitive collegiate tournament forecasting, driving innovation and fostering a more data-informed approach within online communities. Understanding the strengths and limitations of these algorithms is crucial for anyone seeking to leverage the power of Reddit for informed ncaa bracket predictions.
Frequently Asked Questions About Collegiate Tournament Forecasts on Reddit
The following questions address common inquiries regarding the nature, reliability, and utilization of collegiate tournament forecasts shared within online communities, specifically those found on the Reddit platform.
Question 1: What are the primary sources of information used in online collegiate tournament forecasts?
Information sources range from official NCAA statistics and team websites to advanced analytical platforms like KenPom and ESPN Analytics. User-generated content, including scouting reports and injury updates, also contributes to the information pool. The credibility and verification of these sources vary considerably.
Question 2: How can the accuracy of predictions found on these platforms be evaluated?
Forecast accuracy is typically assessed by comparing predicted outcomes with actual tournament results. Metrics such as bracket challenge scores, upset prediction rates, and overall agreement with expert consensus are commonly employed. The historical performance of individual users or forecasting models can also serve as an indicator of reliability.
Question 3: Is it advisable to blindly follow the consensus forecasts found in these online communities?
Blindly following consensus forecasts is not recommended. While collective wisdom can be valuable, these communities are susceptible to biases and groupthink. A critical evaluation of the underlying rationale and methodologies supporting the consensus is essential.
Question 4: What are the potential risks of relying on information shared within online collegiate tournament communities?
Potential risks include the propagation of misinformation, the amplification of biases, and the overestimation of forecast accuracy. Furthermore, the reliance on information shared within these communities can lead to herding behavior, where individuals make similar predictions, potentially reducing the diversity and accuracy of overall bracket selections.
Question 5: How do statistical models contribute to the accuracy of online collegiate tournament predictions?
Statistical models provide a quantitative framework for assessing team strengths and simulating tournament outcomes. These models can incorporate a multitude of variables, such as team efficiency ratings, strength of schedule, and historical performance data, leading to more data-informed and potentially more accurate predictions than purely subjective assessments.
Question 6: Does the sharing of forecast information within these communities influence betting markets?
The sharing of forecast information within online communities can indirectly influence betting markets. Increased public awareness of, and confidence in, specific team outcomes may lead to a corresponding increase in betting volume, potentially altering the odds offered by bookmakers.
In summary, the utilization of collegiate tournament forecast communities requires a discerning approach, balancing the benefits of collective knowledge with the potential pitfalls of bias and misinformation. A critical evaluation of information sources, methodologies, and community dynamics is crucial for informed decision-making.
The following section will delve into the ethical considerations and best practices for participating in online collegiate tournament forecasting communities.
Tips for Navigating Collegiate Tournament Forecasts on Reddit
To effectively utilize collegiate tournament forecasts found within Reddit communities, a strategic and informed approach is essential. The following tips aim to enhance the decision-making process and improve the overall experience.
Tip 1: Prioritize Data-Driven Analysis. Refrain from relying solely on subjective opinions or popular sentiment. Seek out posts and discussions that incorporate statistical data, such as team efficiency ratings, strength of schedule analysis, and player performance metrics. Evaluate the credibility of the data sources cited and the validity of the analytical methods employed.
Tip 2: Critically Evaluate Consensus Forecasts. While community consensus can provide valuable insights, be wary of groupthink. Actively seek out dissenting opinions and alternative perspectives. Consider the potential for biases within the community and assess the degree to which individual forecasts are grounded in objective analysis rather than personal preferences.
Tip 3: Assess the Expertise of Contributors. Evaluate the credentials and experience of individuals providing forecasts. Look for users who demonstrate a consistent track record of accurate predictions or who possess specialized knowledge of specific teams or conferences. Be cautious of individuals who make unsubstantiated claims or who lack a clear understanding of statistical principles.
Tip 4: Scrutinize Upset Predictions. Identifying potential upsets is a crucial aspect of successful bracket construction. However, avoid overemphasizing upset predictions at the expense of overall bracket stability. Carefully assess the rationale behind each upset selection, considering factors such as matchup advantages, key player injuries, and statistical anomalies. Avoid selecting upsets solely based on gut feelings or anecdotal evidence.
Tip 5: Consider Multiple Forecasting Models. Refrain from relying on a single forecasting model or methodology. Explore a variety of models, including those based on statistical regression, machine learning, and expert opinion. Compare the predictions generated by different models and identify areas of agreement and disagreement. Integrate the insights from multiple models to form a more comprehensive and nuanced forecast.
Tip 6: Evaluate the Recency of Information. The collegiate basketball landscape is dynamic, with player injuries, coaching changes, and shifting team dynamics impacting performance. Verify that the information used to generate forecasts is current and reflects the most recent developments. Pay close attention to injury reports, news articles, and statistical updates that may influence team projections.
Tip 7: Acknowledge the Inherent Uncertainty. Collegiate tournament outcomes are inherently unpredictable. Even the most sophisticated forecasting models cannot account for all possible variables. Accept that upsets and unexpected results are inevitable, and adjust bracket selections accordingly. Avoid placing undue confidence in any single prediction and maintain a balanced perspective.
Following these tips can lead to more informed decisions when browsing online collegiate tournament predictions, increasing comprehension and engagement with discussions related to “ncaa bracket predictions reddit.”
In conclusion, navigating online collegiate tournament forecast communities requires a combination of analytical rigor, critical thinking, and an awareness of the inherent limitations of prediction. A thoughtful and disciplined approach will maximize the benefits and minimize the risks associated with relying on information shared within these online spaces.
NCAA Bracket Predictions on Reddit
The analysis of “ncaa bracket predictions reddit” reveals a complex ecosystem of shared information, statistical analysis, and community-driven forecasts. The quality of predictions generated within these online spaces varies significantly, contingent upon factors such as data source reliability, the rigor of statistical models employed, and the mitigation of inherent biases. While these communities offer a valuable platform for aggregating diverse perspectives and enhancing predictive accuracy, a critical and discerning approach is essential for effective utilization.
Ultimately, the responsibility lies with individuals to evaluate the merits of the information and methodologies presented within these online forums. The pursuit of informed predictions requires a commitment to analytical rigor, a healthy skepticism towards consensus opinions, and an acknowledgment of the inherent uncertainties associated with collegiate tournament outcomes. Future research might explore the impact of evolving analytical techniques and the dynamic interplay between online communities and betting markets on the overall predictive landscape.
